Letters
High Test-Set Accuracy Is Not Enough
Over the last several decades, driven by a multitude of benchmarks, supervised learning algorithms have become really good at achieving high accuracy on test datasets. As valuable as this is, unfortunately maximizing average test set accuracy isn’t always enough.
Letters
AI Concentrates Power and Wealth
In my letter last week, I alluded to the way AI tends to concentrate power and wealth. This tendency worries me, and I believe it deserves more attention. The U.S. government has been looking into these winner-take-most dynamics at a few leading technology companies...
Letters
AI Versus Human-Level Performance, Part 2
Last week, I wrote about the limitation of using human-level performance (HLP) as a metric to beat in machine learning applications for manufacturing and other fields. In this letter, I would like to show why beating HLP isn’t always the best way to improve performance.
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